Abstract

In particle simulations of semiconductor devices for electro-static discharge (ESD) study at the microscopic level, solving Poisson's equation is an inevitable but time-consuming step. In this work, a deep learning technique is utilized to resolve Poisson's equation for a PN junction under an ESD event, namely using a trained deep neural network (DNN) to predict the potential distribution according to the charge distribution and the boundary condition under a transient ESD excitation. To improve the generalization performance of the DNN, multiple typical ESD curves with different parameters are used as the excitation boundary to generate large amounts of training data with a finite-element method (FEM) solver. After being trained, the DNN is used in the particle simulation to calculate the current response of the PN junction to a new ESD voltage curve that has never been trained before, and the result can perfectly match with that obtained from the FEM solver.

Department(s)

Engineering Management and Systems Engineering

Second Department

Electrical and Computer Engineering

Comments

National Science Foundation, Grant 1916535

Keywords and Phrases

boundary condition; deep learning; particle simulation; PN junction; Poisson's equation; transient ESD excitation

International Standard Book Number (ISBN)

978-172817430-3

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jul 2020

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